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This study aims to examine the effect of faultline based on job responsibility and their interaction with the incentive scheme on knowledge-sharing behavior.

This research is an experimental study with a 2 × 2 factorial design between subjects. Faultline and incentive schemes are manipulated into two groups (strong faultline–weak faultline and group incentive–individual incentives). This study involved 89 undergraduate accounting students as participants.

This research shows that a strong faultline created a strong social identity effect. Hence, the knowledge-sharing behavior among group members tends to be lower than the weak faultline. Knowledge-sharing behavior tends to be higher in group incentive schemes than individual ones. However, there is no support for interactions between incentive schemes and faultline effects on knowledge-sharing behavior. The results indicate that forming a working subgroup based on informational characteristics attributes reduces cooperative behavior and knowledge sharing between groups.

This study adds a new addition to faultline literature by examining the effect of faultline and incentive schemes on knowledge-sharing behavior based on informational characteristics attributes. Previous research on faultline and knowledge sharing was limited and primarily focused on faultlines created by demographic attributes. This study also enriches faultline literature on knowledge-sharing behavior using an experimental design.

This study aims to explore differences in the usability of the Google Drive application based on demographic characteristics, computer skills and use frequency of Google Drive among public library users of Lahore.

The study adopted a quantitative approach and survey-based research method to achieve the study's objectives. The cloud usability model (user perspective) has been used as a theoretical lens to guide the study objectives. It comprises five dimensions of usability – capable, personal, reliable, valuable and secure.

The findings of the study revealed that the usability of Google Drive varied statistically significantly on the basis of depending on the respondents gender, age, academic qualification, computer skills and Google Drive use frequency.

It is a valuable study since it and adds knowledge to existing literature and has implications for practice.

The findings might be helpful for cloud support teams including Google Drive as they can notice the demographic and other differences among users' perceived usability of Google Drive and can enhance certain features of usability which leads attributes to increase its usage among users.

Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

This research intends to investigate the determinants that affect consumers’ purchase intention of electric vehicles (EVs) in Malaysia using an extended theory of planned behaviour (TPB).

Survey data were collected with a sample size of 306. The research used SmartPLS 4.0 structural equation modelling tool to analyse the data. Reliability and validity tests (discriminant and convergent validity) were used and subsequently assessed the measurement and structural models. Mediation analysis was conducted to identify the role of the latent constructs.

The findings indicated that a green purchase attitude plays a complete mediation role in the effect of environmental knowledge on the purchase intention of EVs. In the same notion, the effect of price perception and availability of charging facilities on the purchase intention of EVs passes completely through perceived behavioural control. However, the subjective norm was an insignificant mediator of the impact between government support and EV purchase intention.

This paper helps to examine the latent constructs that impact purchase intention using environmental knowledge, government support, price perception and the availability of charging facilities. Successful green marketing and a sustainable consumerism framework are seen as a booster to promote the usage of EVs in Malaysia.

An extended TPB model has been employed in this research to study the effects of the above-mentioned constructs. The results show that most of the extended constructs are significant in explaining the purchase intention. The empirical results address the gap in the consumer green attitude and provide insight into this area of study.

Due to the environmental concerns in our society, governments are moving towards green purchasing. However, public sector organizations have substantial internal problems. By using a theory of natural resource-based view, this study aimed to analyse the influence of leadership styles and innovation capabilities on green purchasing in the public sector organizations of a developing economy.

Data was collected from 199 purchasing professionals of public sector organizations, and hypotheses were tested through a quantitative method using partial least square-structural equation modelling with the help of SmartPLS version 4 to validate the measurement model.

This study identified that transformational leadership and delegation style of leadership have a significant and positive effect on innovation capability and green purchasing. Similarly, innovation capability mediates the relationship between leadership styles and green purchasing. However, transformational leadership found a more substantial effect on innovation capability and green purchasing than the delegation leadership style.

Results provide insight into the role of leadership styles in green purchasing and innovation capabilities in public sector organizations. Thus, addressing the important issues of how leadership styles and innovative capabilities (IC) can improve green culture, specifically green purchasing, to enhance ecological sustainability.

Results provide insight into the role of leadership styles in green purchasing and innovation capabilities in public sector organizations. Thus, it addresses how leadership styles and IC can improve green culture, specifically green purchasing, to enhance ecological sustainability.

Under the background of low consumer financial knowledge and accumulated credit card liabilities, this study investigates the relationship between financial knowledge and responsible credit card behavior using data from the 2019 China Household Finance Survey (CHFS). From the perspective of consumer economic well-being, this study defines accruing credit card debt to buy houses and cars when loans with lower interest rates are available as irresponsible credit card behavior.

This study uses probit regressions to examine the association between financial knowledge and responsible credit card behavior because the dependent variable is a dummy variable. To alleviate endogeneity problems, this study uses instrument variables and Heckman’s two-step estimation. Furthermore, to explore the potential mediators in this process, this study follows the stepwise regression method. Finally, this study introduces interaction terms to examine whether this association differs in different groups.

The results indicate that financial knowledge is conducive to increasing the probability of responsible credit card behavior. Mediating analyses reveal that the roles of financial knowledge occur by increasing the degree of concern for financial and economic information and the propensity to plan. Moderating analyses show that the effects of financial knowledge on responsible credit card behavior are stronger among risk-averse consumers and in regions with favorable digital access.

This study measures responsible credit card behavior from the perspective of the consumer’s well-being, which enriches practical implications for consumer finance. Furthermore, this study explores the potential mediators influencing the process of financial knowledge that affects responsible credit card behavior and identifies moderators to conduct heterogeneous analyses, which helps comprehensively understand the nexus between financial knowledge and credit card behavior. By achieving these contributions, this study helps to curb the adverse effects of irresponsible credit card behavior on consumers’ well-being and the economic system and helps policymakers promote financial knowledge to fully prevent irresponsible credit card behavior.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

The purpose of this study is to investigate the impact of board demographic diversity on the dividend payout policy in Turkish capital markets.

Using a sample of 67 non-financial companies listed on Borsa Istanbul 100 index from 2013 to 2018, this study examines the influence of board demographic diversity on dividend payout policies in Turkish capital markets. The authors also create a Demographic Board Diversity Index (DBDI) to estimate the composite cognitive diversity. The authors use dividend payment probability, dividend payout ratio, and dividend yield to measure the dividend policy and employ panel logit and tobit regression models.

The results indicate that diversity in nationality, experience and educational background play an influential role in encouraging companies to pay high dividends, while gender, tenure and age diversity are insignificant in affecting dividend payments. The findings also suggest that the DBDI positively affects the companies in formulating the dividend payout policies. Finally, the findings show that the family-owned companies with diverse board members have a negative influence on dividend payment intensity.

The results offer valuable insights for companies and policymakers in emerging markets to develop a more refined governance structure accommodating board demographic diversity attributes to mitigate agency conflicts between controlling and minority shareholders through setting up effective dividend payout policies.

The existing hospitality and tourism literature indicates a discrepancy in the findings related to the socio-demographic variables' impact on hotel employees' socio-emotional factors and behavioral outcomes. This study aims to provide a comprehensive understanding of this topic.

Using a questionnaire, primary data were gathered from hotel employees in the Union Territory of Jammu and Kashmir, India from February to June 2022. A total of 416 valid responses collected through offline mode were used in the data analysis. Multiple linear regressions were done using SPSS V.29.

The findings show that the socio-demographic characteristics of respondents significantly affect socio-emotional factors at work as well as interpersonal deviance and organizational deviance.

Policymakers and hotel managers can implement training and development programs that assist hotel employees with diversified socio-demographic attributes in handling stress, developing their emotional intelligence and minimizing workplace deviance. The study also provides hotel managers with actional recommendations to reduce work–family conflict, social disparity among employees and their emotional exhaustion.

The study adds to the literature with a comprehensive framework regarding the role of various socio-demographic traits in fostering interpersonal deviance, organizational deviance and socio-emotional factors at work.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0304

Holy cities in India are seeing tremendous gentrification. This study aims to investigate the effect of the changing lifestyle of people towards spirituality and the changing lifestyle's impact on consumer buying behavior on properties in Indian holy cities which has not been studied anecdotally.

The research is exploratory in nature. A questionnaire has been sent to collect primary data through SurveyMonkey. Simple random sampling was used to collect a sample of 450 respondents which was also verified using G* software. The data were analyzed using descriptive statistics and partial least square–structured equation modeling (PLS-SEM).

Findings obtained through the structural model using bootstrapping technique suggest that intrinsic and extrinsic factors are attracting tourists leading to an increase in the demand for real estate in holy cities.

The research findings may vary as per the cultural differences and belief in spirituality, which is subject to perceptual biases in different holy cities.

The traditional determinants of property buying behavior are considered inadequate to attract real estate investments. The inclusion of these behavioral aspects – intrinsic and extrinsic factors may improve the investment inflows in India.

Spirituality connects to the concept of behavioral real estate, where the decision to buy property is largely affected by the emotional attachment of people.

This research adds value to fill the gap by finding out the latent determinant – emotional reasons impacting transnational gentrification in India.